The banana data is represented in figure \ref{fig:scatterBanana}.
Classifying this data with a normally distributed bayes 
classifier does not seem like a very good idea in the sense
that we expect the model never to perform exceptionally well. 
If one imagines a gaussian (contour) in figure \ref{fig:scatterBanana}, one 
can see that even the best fit would have data points near wrong
gaussians. 

\begin{figure}[h]
    \centering
    \includegraphics[width=\textwidth]{resources/scatterBanana}
    \caption{75\% Of the two classes of \textit{banana.mat}. 
Class A is represented in red, class B is represented in blue.}
\label{fig:scatterBanana}
\end{figure}

Octave function \texttt{calc1.m} calculates the error rate: $0.106$, 
and the confusion matrix: 

\begin{center}
\begin{tabular}{ c | c c}
& Classified as A & Classified as B \\
\hline
True A & 221 & 29 \\
True B & 24 & 226
\end{tabular}
\end{center}
%  true_A, false_A; 
% false_B, true_B];

The means $\mu$ and the covariance matrices $\Sigma$ can be calculated by
the Octave functions calls \texttt{mean} and \texttt{cov} respectively. 
